This analysis has a lot of moving parts. First, the anonymized mobility data provides counts of visit, counts of visitors, and duration of stay from an origin Census block group to a destination of interest. In our case, the destinations of interest are the 140 points-of-interest that the data vendor has monitored and that correspond to urban green spaces.


Characterizing Census Block Groups

The Richmond Metropolitan Statistical Area consists of:

Cities (FIPS Code) Counties (FIPS Code)
City of Richmond (51760) Amelia County (51007)
City of Petersburg (51730) Caroline County (51033)
City of Hopewell (51670) Charles City County (51036)
City of Colonial Heights (51570) Chesterfield County (51041)
Dinwiddie County (51053)
Dinwiddie County (51053)
Goochland County (51075)
Hanover County (51085)
Henrico County (51087)
King William County (51101)
New Kent County (51127)
Powhatan County (51145)
Prince George County (51149)
Sussex County (51183)

What was the median household income for the Richmond Metropolitan Statistical Area according to the 2019 American Community Survey (ACS) data? We retrieve this value in the code chunk below.

The median household income for Richmond is estimated at $68,529 per the 2015-2019 ACS. Low income block groups were defined as 50% of the area median income ($34,265), while high income block groups were defined as 150% of this value ($102,794).


ggplot(st_as_sf(richmond.msa.bgs.sf)) +
  geom_sf(aes(fill = as.factor(Income.Class))) +
  scale_fill_manual(values = c("grey85", "springgreen4", "darkorange",  "#5B9BD5"), labels = c("Missing Data", "Low Income (50% of median)", "Middle Income", "High Income (150% of median)")) +
  theme(axis.title.x=element_blank(), legend.title = element_blank(), 
        axis.text.y=element_blank(), axis.title.y=element_blank(),axis.text.x=element_blank(),
        axis.ticks = element_blank(), panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(), panel.background = element_rect(fill = "white")) +
  ggtitle(label = "Median Household Income By Census Block Group", 
          subtitle = "Richmond, VA Metropolitan Area") +
  labs(caption="Note: Median household income for the Richmond MSA \n was $68,529 per the 2015-2019 ACS 5-year Estimates.", size = 1.5) +
  theme(plot.caption = element_text(size =8))

ggplot2::ggsave("./Manuscript Images/census_block_groups_by_income_acs_2019.png", width = 12, height = 8, bg = "white")


richmond.msa.bgs.low.income <- richmond.msa.bgs.sf[richmond.msa.bgs.sf$Income.Class == 1, ]
richmond.msa.bgs.mid.income <- richmond.msa.bgs.sf[richmond.msa.bgs.sf$Income.Class == 2, ]
richmond.msa.bgs.hi.income <- richmond.msa.bgs.sf[richmond.msa.bgs.sf$Income.Class == 3, ]


Access to Urban Green Spaces

The location and quality of parks and other urban green spaces are not uniformly distributed within cities and Richmond is no exception. The interactive map below shows the location of the 140 urban green spaces considered shaded according to management type (i.e., who owns and maintains the space) as well as acreage reflected in the size of the point symbol.

parks <- st_read("./Access to UGS/Richmond_POI_Nature_NAICS_712190_Joined_SP_Meters.shp")
## Reading layer `Richmond_POI_Nature_NAICS_712190_Joined_SP_Meters' from data source `C:\Users\bw6xs\Documents\Conferences\UAA\Access to UGS\Richmond_POI_Nature_NAICS_712190_Joined_SP_Meters.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 140 features and 36 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 3513940 ymin: 1093382 xmax: 3677249 ymax: 1228239
## Projected CRS: NAD83 / Virginia South
tmap_mode("view")

tm_shape(richmond.msa.bgs.sf, name = "Census Block Groups") +
  tm_polygons(col = "Income.Class", palette = c("grey75", "springgreen4", "darkorange",  "#5B9BD5"), 
          labels = c("Missing Data", "Low Income (50% of median)", "Middle Income", "High Income (150% of median)"), 
          style = "cat", alpha = 0.8, border.col = "white",
              colorNA = "white",
              textNA = "Missing Data", 
              title = "Income Category",
              popup.vars = c("Percent White: " = "PCTWHITE", "Percent African-American: " = "PCTAFAM", 
                             "Percent Hispanic: " = "PCTHISP")) +
tm_shape(parks, name = "Urban Green Spaces") +
  tm_dots(shape = 21, size = "GISACRE", col = "MATYPE", palette = "Spectral", style = "cat", id = "MANAME", 
                    colorNA = "orange", textNA = "Unknown", title = "UGS",
          popup.vars = c("Name: " = "MANAME", "Size (acres): " = "GISACRE" )) +
  tmap_options(check.and.fix = TRUE) + 
  tm_view(set.view = c(-77.494769, 37.513747, 9)) + 
  tm_basemap(c("Esri.WorldGrayCanvas", "CartoDB.Positron", "Esri.WorldTopoMap", "CartoDB.DarkMatter"))


The World Health Organization (WHO)1 has established a protocol for assessing accessibility to parks and green space that considers proximity and size. Rather than relying on a single threshold, this approach estimates the percentage of the population within 300 meters (0.186 miles) of the boundary of a green space at least 1 hectare (2.47 acres) in size and of at least 0.5 hectare (1.23 acres), with the buffer distance explained as “corresponding to [an] approximately 5 minute walk” (p. 32). A third criterion centers on whether there is a green space of at least 100 hectares (~248 acres) within 5000 meters and of the 744 Census block groups in the Richmond region, only 48 meet all three of these WHO criteria. However, 85 percent (i.e., 41 of 48 block groups) fall within the “Middle Income” or “High Income” categories which is slightly lower than the overall distribution of 87 percent (i.e., 650 of 744 block groups) within the “Middle Income” or “High Income” categories.



We also calculate a cumulative measure based on the gravity model (Hansen, 1959) conceptualization of accessibility adopted from Sharifi et al. (2021):

\[A_{i} = \sum_{j=1}^{n} \frac{Size_{j}}{e^{D_{ij}}}\]
In the above equation, A is an accessibility index for Census block group i calculated as the sum of the size of green space j divided by the walking time between the centroid of Census block group i to the boundary of green space j such that more remote destinations contribute less to residents’ green space accessibility, but are still considered. Accounting for accessibility of green space within the study is crucial because prior research has shown that there are significant inequities in access due to the uneven distribution of green space in urban areas.


Of the 186 Census block groups within the upper quartile (i.e., most access) of UGS potential Ai 164 (i.e., 88 percent) fall within the “Middle Income” or “High Income” categories.


Changes in Mobility

Reported cases of COVID-19 increased within the Richmond MSA from March until July of 2020, while hospitalizations peaked in late April.


During the early days of the COVID-19 pandemic people traveled less due in large part to government-mandated lockdowns and the abrupt pivot to online education and work. We use anonymized GPS data collected from mobile devices and provided by SafeGraph to explore general patterns of mobility within the Richmond metropolitan area before and after the March 2020 lockdown was instituted.

It should be noted that the mobility data used for this study were derived from mobile devices through passive sensing, where data are captured without direct effort on the part of users. As a result, the proportion of residents in a given Census block group who are actively contributing data varies over time. For context, total coverage for the entire Richmond region in March of 2018 was 135,768 mobile devices for an estimated total population of 1,293,876 residents or 10.5 percent. In March of 2019 coverage increased to about 11.0 percent and fell back to 10.5 percent in March of 2020. The image below shows the number of devices contributing data as a percentage of total population of each Census block group on the Monday immediately after the March 12th lockdown began:



This reduction in mobility is apparent if we examine the median percentage of time mobile devices were observed at home on the Monday two weeks before the March 12th lockdown began:



Versus the Monday immediately after the March 12th lockdown began:



Compared with the Monday two weeks after the March 12th lockdown began:



We can also see that the amount of time spent at home after the lockdown was imposed was mediated by income and race with mobile devices based in Low Income Census block groups spending much less time at home than their counterparts in Middle Income and High Income Census block groups. It is also clear that mobile devices based in Census blocks with a higher percentage of residents identifying as White only tended to spend more time at home during this period.


Changes in UGS Usage

Outdoor activities like visiting parks and other urban green spaces were among the only options for active and passive recreation that continued to function and did not involve coming into close physical contact with other people who might be a source of infection. We can ask How did the number of visits to UGS change before and after the March 2020 lockdown was instituted? and explore this question using the anonymized GPS data collected from mobile devices and provided by SafeGraph. In these datasets, a mobile device must spend at least 4 minutes at a given point of interest (POI) in order to count as a visit.

The pair of barcharts below show that the number of visitors to UGS in the analysis sample declined considerably for mobile devices based in Low Income Census block groups between the first and last weeks of March 2020. Similarly, the grouped barchart shows the number of visitors to UGS in the analysis sample as a percentage—this is an important distinction given that mobility declines and the number of visitors to UGS dropped across the board during the lockdown period.



Neither the UGS access potential metric nor the WHO criteria not appear to be related to these changes in UGS usage and so the statistics and visualizations exploring these relationships are omitted here.



We might expect that Census block groups with greater access to UGS (i.e., physical proximity and/or lower travel time) may have made more visits or stayed longer at these destinations and we can explore this question using the anonymized GPS data collected from mobile devices and provided by SafeGraph. Because the available data only provide a median dwell time in minutes, these are visualized as a series of scatterplots showing each week in March and April of 2020 alongside the corresponding week in March and April of 2019. The SafeGraph documentation define the median dwell time data element as follows:

This is the median of the minimum dwell times we have calculated for each of the visits to the POI (point of interest). We determine the minimum dwell time by looking at the first and last ping we see from a device during a visit. This is a minimum dwell because it is possible the device was at the POI longer than the time of the last ping.

What I see in the plots below is that with the pandemic lockdown, the mobile devices contributing data to SafeGraph frequented UGS in the analysis sample closer to their home Census block group and by the end of April 2020, were staying at the UGS destinations longer. Of course there are other determinants at work including differences in weather.

















Survey of Selected Census Block Groups

Survey Findings

Structural Equations Model (SEM)

SEM Findings

Takeaways

  1. World Health Organization—WHO. (2016). Urban green spaces and health: A review of evidence. Copenhagen, Denmark: WHO Regional Office for Europe.↩︎